Open Source


Using Open Source Tools to Push Metrics into LogicMonitor

Ever walk into a corner market, push on the door and find it won’t open? You look down at the handle and are reminded by a sign on the door that you have to “pull” to open it? The LogicMonitor platform uses an agentless collector to pull metrics from thousands of devices and resources into a unified monitoring view (no agents required). We currently offer more than 2,000 LogicModules out-of-the-box that gather metrics from all kinds of systems using many different protocols.


Open Source Application Monitoring with OpsRamp

How critical are open source applications for modern application and infrastructure modernization? Red Hat’s The State of Enterprise Open Source report found that more than two-thirds of IT leaders believe that open source software has a very important role to play in the enterprise and 59% of respondents expect to increase their use of open source in the next 12 months.


A humble and sincere 'thank you' to our open source community

Since our inception, open source contributions have been integral to our business and success. This October, as we participated in the great Hacktoberfest event, organized by Digital Ocean and hosted by GitHub and this month as we host our own hackathon event, we reflect upon and appreciate our community.

A Guide to Open Source Monitoring Tools

Open source is one of the key drivers of DevOps. The need for flexibility, speed, and cost-efficiency, is pushing organizations to embrace an open source-first approach when designing and implementing the DevOps lifecycle. Monitoring — the process of gathering telemetry data on the operation of an IT environment to gauge performance and troubleshoot issues — is a perfect example of how open source acts as both a driver and enabler of DevOps methodologies.


Introducing 'MLWatcher', Anodot's Open-Source Tool For Monitoring Machine Learning Models

Machine Learning (ML) algorithms are designed to automatically build mathematical models using sample data to make decisions. Rather than use specific instructions, they rely on patterns and inference instead. And the business applications abound. In recent years, companies such Google and Facebook have found ways to use ML to utilize the massive amounts of data they have for more profit.